Abstract
Twitter plays a substantial role in shaping the online news media landscape, which is established on the interconnections between various actors. This research conducts network analysis in Twitter with the aim to explore Turkey’s polarized news media landscape in the aftermath of the Gezi protests. The findings, based on a relational approach, are analyzed at four levels: identifying the actors, positioning the actors and examining connections among their clusters, exploring the patterns of news diffusion, and reviewing media polarization. It has been found that distinct conditions such as intermedia differences apply to each one of those levels, while polarization is evident in the network.
Introduction
Social media play a substantial role in shaping the field of journalism. Among social media, particularly Twitter is considered to have a significant potential for the benefit of news and journalism. While it is one of the many platforms in which the news media operate, Twitter is not typical as its topological characteristics make it more akin to a broadcast medium (Cha et al., 2012). For this reason, it is associated with the ongoing transformations in the news media industry (Broersma and Graham, 2013; Hermida, 2010a; Hong, 2012; Kwak et al., 2010; Poell and Borra, 2012). Twitter reorganizes the online news media landscape through the interconnections among a variety of actors, and thanks to its self-evident configuration, those connections of news media can be directly observed unlike in other domains. This enables us to see the broader picture of the media landscape, and to review evidence on the diffusion of news and polarization.
Twitter challenges the relationship between news producers and readership. In this relationship, it is not only the readers who redefine conventions of existing news environment but also the stakeholders in the media industry strive to secure their position as the providers of news. They establish particular connections through this social networking site (SNS) as they remain to be the main actors of the news environment. In contrast to conventional news media, those connections help develop a multifaceted ground, while the use of Twitter by journalists, news organizations, and individual users creates a complex and networked system of social awareness (Papacharissi and de Fatima Oliveira, 2012: 268). This system of social awareness is to a great extent defined by the connections between a wide variety of actors in the news environment.
In journalism literature relating to the SNSs, Twitter is often considered as an awareness system which refers to a framework ‘intended to help people construct and maintain awareness of each others’ activities, context or status, even when the participants are not co-located’ (Markopoulos et al., 2009). Awareness, in this context, applies to a large setting in which three levels of relationships are observed: (1) the relationship between news media and its audiences, (2) the relationship among audiences, and (3) the relationship among media institutions. Any sort of news available in the Twittersphere circulates at those three levels that build up the awareness system. This research particularly focuses on the third level to reveal the news media network in Twitter through which the news content is diffused.
Hermida (2010b: 298) draws from computer science literature to suggest that the broad, asynchronous, lightweight and always-on systems are enabling citizens to maintain a mental model of news and events around them, giving rise to awareness systems described as ambient journalism. He suggests that ambient journalism presents a multifaceted and fragmented news experience, where small pieces of content can be collectively considered as journalism (Hermida, 2010a). Burns (2010) modifies Hermida’s definition of ambient journalism, describing it as ‘an emerging analytical framework for journalists’ which informs the design of real-time platforms for journalistic sources and news delivery. Twitter, as a stage for the expression of ambient journalism, serves as a platform, where users receive a flow of information from both established media and each other (Hermida, 2010a).
According to Hermida (2010b: 301), established journalism is based on content-oriented communication, whereas Twitter adds an additional layer that can be considered as what Kuwabara et al. (2002) refer to as connectedness-oriented communication. The communication infrastructure based on Twitter algorithms technically provides a platform for the spreading of content. In the case of media organizations, this infrastructure defines the setting for the audience networks through which news content circulates. This technical infrastructure also includes the parameters for linkages from a relational approach and sets the ground for the construction of meanings and dialogues in the media landscape.
While tweets are atomic in nature, they are part of a distributed conversation through a social network of interconnected users (Hermida, 2010a). In this conversation, the value does not lie in the individual piece of information, rather it lies in the combined effect of the communication (Hermida, 2010a). From this perspective, boundaries of the networked news sphere in Twitter can be drawn by considering the aggregate accounts of news media and the total number of tweets shared from those accounts. But the social graph of media accounts is also embedded within the news sphere. The SNSs allow users to (1) construct a public or semi-public profile within a bounded system, (2) articulate a list of other users with whom they share a connection, and (3) view and traverse their list of connections and those made by others within the system (Boyd and Ellison, 2007: 211). All the data deriving from those functions make up a social graph for each user account, which can be added to the conversational network in Twitter.
From their analysis, Java et al. (2007) find that the main types of user intentions in Twitter are daily chatter, conversations, sharing information, and reporting news. These motivations for Twitter use correspond to Deuze’s (2003: 205) typology of online journalisms in which share and discussion sites are highlighted by their features such as concentration on public connectivity and unmoderated participatory communication. Twitter, in that sense, supports a conversational network structure for journalistic priorities because its information architecture is designed to adopt an ‘asymmetric model’ of relationships allowing a user to follow only a couple of chosen accounts while being followed by millions. According to Chen (2009), one-way following adds depth and simple segmentations to a user’s friend list in which four tiers of relationships can be observed:
People who follow you but you do not follow back;
People who do not follow you but you follow them;
You both follow each other;
Neither of you follow each other.
Twitter’s subscription network further supports this one-way following mechanism (Figure 1). An account (X) may have followers at the first tier (A and B), and those followers may be followed by other accounts at the second tier (C, D, and E), which ultimately builds up an enormous social network connecting a great number of users. There is a high probability for the news media accounts in Twitter that they will be followed by a large number of users, multiplied by other users at sequential tiers, and their connections will eventually put together the news media landscape in Twitter.

Twitter’s subscription network.
Twitter provides an enormous potential for journalism scholarship as more than half of its users turn to this social networking platform for news (Pew Research Center, 2013). In Turkey, Twitter has become an important playground for the news media, particularly following its wide use during the Gezi protests. While the mainstream media failed to report on the occurring events and Twitter changed the source of news for many, Gezi marked the milestone of Turkey’s news environment and revealed the patterns of political climate in the country. Twitter in Turkey is currently a polarized stage with 10.3 million active users (GlobalWebIndex, 2015), where diverse actors from both mainstream and nonmainstream media come together to grow their echo chambers in the contemporary news environment. This research explores Turkey’s news media landscape in Twitter with a particular focus on polarization and examines the sorts of connections among a variety of actors to give a map of the post-Gezi environment.
Mapping the news media in Twitter allows us to evaluate this domain in several ways. First, revealing the connections helps determine the prominent actors in Twitter’s news sphere. Second, focusing on the diversity of actors, the analysis gives us the opportunity to examine connections regarding different media categories, such as mainstream organizations and alternative media. Third, positioning the actors and analyzing the interaction among clusters enable us to monitor the channels through which news is disseminated. This gives evidence on the patterns of news circulation, gatekeeping, and institutional policies of affiliation. Finally, it provides us with the necessary information to observe the political stance of media actors and allows us to find out whether Turkey’s often-mentioned problem of polarization prevails correspondingly in the Twittersphere.
Methodology
Social media are useful for studying the media landscape because all interactions from both news media and audiences are recorded online (An et al., 2011). Twitter, in particular, enables researchers to retrieve open data, which can be sited and organized in a spreadsheet, allowing both quantitative and qualitative analyses. This research explores the media landscape in Twittersphere with respect to the interconnections between news media accounts. The diffusion of news in Twitter and the potential indications of polarization are reviewed considering the connections among a sample of news media accounts.
The methodology of this research is network analysis. As the initial step, several Twitter accounts among the common mainstream and nonmainstream media in Turkey were selected to create the research sample. The sample was snowballed based on the set of following accounts to include additional accounts. Then the number of tweets, followings, and followers of each account was identified to provide basic information, and the connections among the actors were mapped regarding their followings and followers.
After having manually coded the data in a spreadsheet, Gephi was used to visualize the interconnections among the accounts. A directed layout was preferred in order to track the news flow and to demonstrate the distinction between the following and follower accounts. ForceAtlas 2 was employed as the layout algorithm to provide a meaningful map of connections. 1 Drawing on the connections in the network, indegrees and outdegrees of actors were revealed and compared. Actors in the network were color coded regarding their media category such as mainstream media, activist media, ethnic media, and women’s media. Their connections were analyzed regarding media concentration and political tendencies. Secondary sources such as websites, news articles, and published reports were consulted to get additional information on the peculiarities of relevant news media. Further analysis on media categories was conducted to find out intermedia relations within the network. Eventually, the positioning of actors was explored with the aim to shed light on Turkey’s news media landscape in Twitter.
The relational network presented in this exploratory study should be evaluated with consideration of two limitations. First, Turkey’s news media network in Twitter was mapped with regard to the feature of following, which is an indication of the present connections between accounts. However, this does not mean that following in Twitter results in solid relationships among organizations. Second, this study is limited in terms of the included news media accounts. Individual accounts of journalists were omitted as they have their own network of followers and build up another layer based on their individual disposition. Several media accounts that aggregate and amplify news instead of creating their own content were also excluded from the research, along with news magazines, local papers, and suspended accounts. The relational map provided in this research includes the major actors, but it should not be taken to represent the entire news sphere in Turkey.
Findings
This research examines the media network in Twitter at four levels. First, the research sample is introduced to set the context for the media landscape in Twitter. Media accounts from a wide variety of categories are listed along with their profile information including the number of tweets, following, and followers. Second, network analysis is conducted to investigate their positionings in relation to other accounts in the network. Third, patterns of news diffusion and gatekeeping practices of media accounts are explored. And finally, their political stance is evaluated relying on the analysis of clusters in the network.
Setting the scene: Actors of the media landscape in Twitter
Only until the last half-decade, Turkey had a vibrant media industry with hundreds of television channels, thousands of local and national radio stations, several dozen newspapers, and a rapidly growing infrastructure for broadband Internet (Tuncel, 2011). However, the Justice and Development Party (AKP) fashioned a news environment by creating its own pro-AKP outlets and closed >100 media organizations (3 news agencies, 16 television stations, 23 radio stations, 45 newspapers, 15 magazines, and 29 publishing houses and distribution companies) following the failed coup attempt in July 2016 (Akin, 2016). Twitter is an aspect of this environment, where leading stakeholders of the media industry in Turkey come to claim their domain, while media organizations that are smaller in scale sustain their operations. Twitter has also been the birthplace of several nonmainstream organizations founded in the aftermath of the Gezi protests.
Twitter accounts used in this research are listed in Table 1 along with the number of tweets, followings, and followers. 2 Diverse qualities are observed among the sample organizations, and for this reason, they were sorted into categories as mainstream media, dissident institutional media, civic media, activist media, news agencies, ethnic media, women’s media, and foreign media. 3 Mainstream media represent the renowned institutions that are owned by stakeholders, who also have investments in sectors other than the media. These are all pro-AKP institutions, except for CNN Türk and Sözcü. Dissident institutional media do not take part in large media conglomerates and are usually critical of the government. Civic media are usually steered by media professionals, and some of them are financially supported by foundations. Activist media are operated by citizens. Many of them have been founded during and after the Gezi protests. News agencies are affiliated with a variety of media groups. Ethnic media are owned and operated by the Kurds and Armenians living both inside and outside the borders of the country. Women’s media, as implied by its name, represent women and give priority to gender-related issues. Finally, foreign media have their headquarters outside of Turkey and have specialized sections to broadcast in Turkish. In this picture, the existing mainstream stakeholders of the media industry take advantage of their popularity, whereas alternative news media strive to increase their awareness in Twitter. In the meantime, several accounts of citizens’ media pop up to grow a subdomain in this vast arena.
List of sample accounts in Twitter (2 December 2015).
| Account owner | User name | Tweets | Following | Followers |
|---|---|---|---|---|
| ★NaberMedya TV | @Revoltistanbul | 15.8K | 1453 | 14.3K |
| 140journos | @140journos | 29K | 12.8K | 78.9K |
| A Haber | @tvahaber | 184K | 29 | 630K |
| acikradyo | @acikradyo | 7467 | 352 | 141K |
| AGOS/ԱԿՕՍ | @AGOSgazetesi | 21.6K | 35 | 156K |
| Aksam.com.tr | @Aksam | 86K | 12 | 461K |
| Al Jazeera Türk | @AJTurk | 46.1K | 10 | 352K |
| Anadolu Ajansı | @anadoluajansi | 112K | 21 | 612K |
| Anarşi Haber | @AnarsiHaber | 9171 | 257 | 4697 |
| artıbirtv | @artibirtv | 21.7K | 22 | 81.2K |
| Aydınlık Gazetesi | @AydinlikGazete | 57.3K | 95 | 235K |
| Başka Haber | @baskahaber | 44.6K | 5 | 26.2K |
| BBC Türkçe | @bbcturkce | 67.2K | 31 | 1.6M |
| bianet | @bianet_org | 64.3K | 24 | 147K |
| BirGün Gazetesi | @BirGun_Gazetesi | 87.9K | 5 | 514K |
| Bugün TV | @BugunTv | 16.5K | 49 | 228K |
| Çapul TV | @capul_tv | 17.4K | 5 | 165K |
| Cihan Haber Ajansı | @Cihan_Haber | 57.6K | 0 | 787K |
| CNN Türk | @cnnturk | 135K | 107 | 2.89M |
| cumhuriyet.com.tr | @cumhuriyetgzt | 164K | 72 | 963K |
| DAILY SABAH | @DailySabah | 29.6k | 71 | 36.7K |
| Demokrat Haber | @DemokratHaber | 53.1K | 830 | 53.9K |
| Dengê Kurdistan | @R_D_Kurdistan | 23.7K | 402 | 22.6K |
| Dicle Haber Ajansı | @DicleHaberAjans | 23.8K | 0 | 204K |
| Diken | @DikenComTr | 60.4K | 37 | 257K |
| Doğan Haber Ajansı | @dhainternet | 123K | 6 | 676K |
| dokuz8 | @dokuz8haber | 16.3K | 2418 | 37.1K |
| DW TÜRKÇE | @dw_turkce | 30K | 6 | 15.8K |
| ETHA | @etkinhaberajans | 40.6K | 0 | 31.9K |
| Evrensel Gazetesi | @evrenselgzt | 98.1K | 85 | 212K |
| Fırat Haber Ajansı | @ANFTTURKCE | 26.6K | 12 | 98.2K |
| Gerçek Gündem | @gercekgundemcom | 78.5K | 1544 | 82.8K |
| Güneş | @gunes_gazetesi | 16.4K | 7 | 350K |
| Haber 7 | @Haber7 | 70.2K | 5 | 567K |
| Habertürk TV | @HaberturkTV | 93.4K | 29 | 569K |
| Hayat Televizyonu | @hayat_tv | 36.3K | 61 | 99.1K |
| Hurriyet Daily News | @HDNER | 69.5K | 45 | 159K |
| Hurriyet.com.tr | @Hurriyet | 134K | 41 | 2.74M |
| İhlas Haber Ajansı< | @ihacomtr | 55.2K | 83 | 337K |
| imc tv | @imc_televizyonu | 45.8K | 13 | 393K |
| İstanbul Indymedia | @Istanbul_Indy | 10.6K | 2387 | 13.4K |
| JIN HABER AJANSI | @jinhaberajans | 23.6K | 195 | 47.6K |
| Jiyan | @jiyaninsesi | 18.6K | 1827 | 31.7K |
| Kamera Sokak | @KameraSokak | 9799 | 267 | 13.8K |
| Kazete_KadınGazetesi | @Kazete_ | 33.6K | 666 | 5146 |
| Millet Gazetesi | @Ozgur_millet | 15.3K | 23 | 73.5K |
| milliyet.com.tr | @milliyet | 128K | 37 | 1.63M |
| NTV | @ntv | 85.8K | 51 | 4.94M |
| Odatv | @odatv | 107K | 28 | 617K |
| Ötekilerin Postası | @otekilerpostasi | 127K | 442 | 234K |
| Özgür Gündem | @ozgurgundemweb | 21.3K | 50 | 140K |
| P24 | @P24Punto24 | 14K | 935 | 9277 |
| Posta Gazetesi | @postacomtr | 94.2K | 71 | 63.5K |
| Radikal | @radikal | 147K | 44 | 943K |
| Sabah Gazetesi | @Sabah | 98.5K | 24 | 1.01M |
| Samanyolu Haber TV | @SHaberTV | 38.6K | 1 | 448K |
| sendika.org | @sendika_org | 127K | 9 | 182K |
| soL Haber Portalı | @solhaberportali | 140K | 6 | 421K |
| Sözcü | @gazetesozcu | 86.2K | 27 | 780K |
| Sputnik Türkiye | @sputnik_TR | 42.3K | 11 | 42.6K |
| STAR | @stargazete | 160K | 44 | 719K |
| STÊRK TV | @TVsterkTV | 2776 | 30 | 79.9K |
| Şalom Gazetesi | @SALOMgazetesi | 9408 | 108 | 10.9K |
| T24 | @t24comtr | 79K | 4 | 500K |
| Takvim | @takvim | 79K | 7 | 39.3K |
| Taraf Gazetesi | @Taraf_Medya | 34.6K | 1 | 417K |
| TGRT Haber TV | @tgrthabertv | 61.7K | 20 | 214K |
| Today’s Zaman | @todayszamancom | 85.1K | 61 | 204K |
| TRT HABER | @trthaber | 45K | 28 | 1.18M |
| TRT TÜRK | @trtturk | 158K | 25 | 169K |
| Türkiye Gazetesi | @turkiyegazetesi | 82.6K | 60 | 136K |
| Turkiye Newspaper | @tgnewspaper | 2708 | 48 | 1006 |
| tvnet | @tvnet | 37.4K | 22 | 111K |
| Ulusal Kanal | @ulusalkanal | 92.6K | 16 | 414K |
| ÜLKE TV | @ulketv | 14.6k | 16 | 219K |
| VagusTV | @Vagustv | 13.5K | 68 | 21.1K |
| Vatan | @Vatan | 107K | 25 | 121K |
| Yarın | @yarinhaber | 67.6K | 220 | 19.5K |
| Yeni Şafak | @yenisafak | 137K | 41 | 474K |
| Yüksekova Haber | @YuksekovaHaber | 114K | 3747 | 44.5K |
| Yurt Gazetesi | @yurtgazetesi | 98K | 7 | 151K |
| YURTSUZ | @yurtsuzhaber | 2696 | 227 | 573 |
| Zaman Gazetesi | @zamancomtr | 118K | 65 | 980K |
| zete | @zetegazete | 46K | 174 | 24.9K |



